


'''simple docstring'''


import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return input_array.reshape((input_array.size, 1)	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.nan
				for i in range(SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			Tuple							= features[:, labels == i]
								_SCREAMING_SNAKE_CASE					:			str							= data.mean(1	)
								# Centralize the data of class i
								_SCREAMING_SNAKE_CASE					:			Any							= data - column_reshape(SCREAMING_SNAKE_CASE__	)
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += np.dot(SCREAMING_SNAKE_CASE__				, centered_data.T	)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= np.dot(SCREAMING_SNAKE_CASE__				, centered_data.T	)

				return covariance_sum / features.shape[1]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= features.mean(1	)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.nan
				for i in range(SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			Any							= features[:, labels == i]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= data.shape[1]
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= data.mean(1	)
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += device_data * np.dot(
												    column_reshape(SCREAMING_SNAKE_CASE__	) - column_reshape(SCREAMING_SNAKE_CASE__	)				, (column_reshape(SCREAMING_SNAKE_CASE__	) - column_reshape(SCREAMING_SNAKE_CASE__	)).T				, )
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												_SCREAMING_SNAKE_CASE					:			List[Any]							= device_data * np.dot(
												    column_reshape(SCREAMING_SNAKE_CASE__	) - column_reshape(SCREAMING_SNAKE_CASE__	)				, (column_reshape(SCREAMING_SNAKE_CASE__	) - column_reshape(SCREAMING_SNAKE_CASE__	)).T				, )

				return covariance_sum / features.shape[1]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if features.any():
								_SCREAMING_SNAKE_CASE					:			List[str]							= features.mean(1	)
								# Center the dataset
								_SCREAMING_SNAKE_CASE					:			List[Any]							= features - np.reshape(SCREAMING_SNAKE_CASE__				, (data_mean.size, 1)	)
								_SCREAMING_SNAKE_CASE					:			str							= np.dot(SCREAMING_SNAKE_CASE__				, centered_data.T	) / features.shape[1]
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= np.linalg.eigh(SCREAMING_SNAKE_CASE__	)
								# Take all the columns in the reverse order (-1), and then takes only the first
								_SCREAMING_SNAKE_CASE					:			List[Any]							= eigenvectors[:, ::-1][:, 0:dimensions]
								# Project the database on the new space
								_SCREAMING_SNAKE_CASE					:			Any							= np.dot(filtered_eigenvectors.T				, SCREAMING_SNAKE_CASE__	)
								logging.info("""Principal Component Analysis computed"""	)

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR				, format="""%(message)s"""				, force=SCREAMING_SNAKE_CASE__	)
								logging.error("""Dataset empty"""	)
								raise AssertionError







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= eigh(
								    covariance_between_classes(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)				, covariance_within_classes(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)				, )
								_SCREAMING_SNAKE_CASE					:			str							= eigenvectors[:, ::-1][:, :dimensions]
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Any							= np.linalg.svd(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= svd_matrix[:, 0:dimensions]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= np.dot(filtered_svd_matrix.T				, SCREAMING_SNAKE_CASE__	)
								logging.info("""Linear Discriminant Analysis computed"""	)

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR				, format="""%(message)s"""				, force=SCREAMING_SNAKE_CASE__	)
								logging.error("""Dataset empty"""	)
								raise AssertionError







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]	)
				_SCREAMING_SNAKE_CASE					:			str							= np.array([0, 0, 0, 1, 1]	)
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 2
				_SCREAMING_SNAKE_CASE					:			List[str]							= 2

				# Assert that the function raises an AssertionError if dimensions > classes
				with pytest.raises(SCREAMING_SNAKE_CASE__	) as error_info:
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= linear_discriminant_analysis(
								    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								if isinstance(SCREAMING_SNAKE_CASE__				, np.ndarray	):
												raise AssertionError(
												    """Did not raise AssertionError for dimensions > classes"""	)
								assert error_info.type is AssertionError







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 2
				_SCREAMING_SNAKE_CASE					:			List[str]							= np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]	)

				with pytest.raises(SCREAMING_SNAKE_CASE__	) as error_info:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= principal_component_analysis(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								if not np.allclose(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
												raise AssertionError
								assert error_info.type is AssertionError


if __name__ == "__main__":
				import doctest

				doctest.testmod()








'''simple docstring'''


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_						:  Optional[Any]				  =						16
UpperCAmelCase_						:  List[str]				  =						32







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = 16				, SCREAMING_SNAKE_CASE__ = "bert-base-cased"	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= load_dataset("""glue"""				, """mrpc"""	)

				def tokenize_function(SCREAMING_SNAKE_CASE__	):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer(examples["""sentence1"""]				, examples["""sentence2"""]				, truncation=SCREAMING_SNAKE_CASE__				, max_length=SCREAMING_SNAKE_CASE__	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_SCREAMING_SNAKE_CASE					:			str							= datasets.map(
				    SCREAMING_SNAKE_CASE__				, batched=SCREAMING_SNAKE_CASE__				, remove_columns=["""idx""", """sentence1""", """sentence2"""]				, load_from_cache_file=SCREAMING_SNAKE_CASE__	)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_SCREAMING_SNAKE_CASE					:			Tuple							= tokenized_datasets.rename_column("""label"""				, """labels"""	)

				def collate_fn(SCREAMING_SNAKE_CASE__	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""max_length"""				, max_length=128				, return_tensors="""pt"""	)
								return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""longest"""				, return_tensors="""pt"""	)

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE					:			int							= DataLoader(
				    tokenized_datasets["""train"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= DataLoader(
				    tokenized_datasets["""validation"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)

				return train_dataloader, eval_dataloader







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				model.eval()
				_SCREAMING_SNAKE_CASE					:			Dict							= 0
				for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								with torch.no_grad():
												_SCREAMING_SNAKE_CASE					:			Tuple							= model(**SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= outputs.logits.argmax(dim=-1	)
								# It is slightly faster to call this once, than multiple times
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= accelerator.gather(
								    (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(SCREAMING_SNAKE_CASE__	) - 1:
																_SCREAMING_SNAKE_CASE					:			List[str]							= predictions[: len(eval_dataloader.dataset	) - samples_seen]
																_SCREAMING_SNAKE_CASE					:			List[Any]							= references[: len(eval_dataloader.dataset	) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=SCREAMING_SNAKE_CASE__				, references=SCREAMING_SNAKE_CASE__				, )

				_SCREAMING_SNAKE_CASE					:			str							= metric.compute()
				return eval_metric["accuracy"]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= config["""lr"""]
				_SCREAMING_SNAKE_CASE					:			Any							= int(config["""num_epochs"""]	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= int(config["""seed"""]	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= int(config["""batch_size"""]	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= args.model_name_or_path

				set_seed(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_dataloaders(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__				, return_dict=SCREAMING_SNAKE_CASE__	)

				# Instantiate optimizer
				_SCREAMING_SNAKE_CASE					:			List[str]							= (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_SCREAMING_SNAKE_CASE					:			Any							= optimizer_cls(params=model.parameters()				, lr=SCREAMING_SNAKE_CASE__	)

				if accelerator.state.deepspeed_plugin is not None:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 1
				_SCREAMING_SNAKE_CASE					:			int							= (len(SCREAMING_SNAKE_CASE__	) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= get_linear_schedule_with_warmup(
								    optimizer=SCREAMING_SNAKE_CASE__				, num_warmup_steps=0				, num_training_steps=SCREAMING_SNAKE_CASE__				, )
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= DummyScheduler(SCREAMING_SNAKE_CASE__				, total_num_steps=SCREAMING_SNAKE_CASE__				, warmup_num_steps=0	)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Tuple							= accelerator.prepare(
				    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# We need to keep track of how many total steps we have iterated over
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0
				# We also need to keep track of the stating epoch so files are named properly
				_SCREAMING_SNAKE_CASE					:			str							= 0
				_SCREAMING_SNAKE_CASE					:			Tuple							= evaluate.load("""glue"""				, """mrpc"""	)
				_SCREAMING_SNAKE_CASE					:			int							= num_epochs

				if args.partial_train_epoch is not None:
								_SCREAMING_SNAKE_CASE					:			Dict							= args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint	)
								_SCREAMING_SNAKE_CASE					:			Any							= args.resume_from_checkpoint.split("""epoch_"""	)[1]
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """"""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_SCREAMING_SNAKE_CASE					:			int							= int(SCREAMING_SNAKE_CASE__	) + 1
								_SCREAMING_SNAKE_CASE					:			List[str]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint performance:"""				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint's scheduler's lr:"""				, lr_scheduler.get_lr()[0]	)
								accelerator.print("""resumed optimizers's lr:"""				, optimizer.param_groups[0]["""lr"""]	)
								with open(os.path.join(args.output_dir				, f"""state_{starting_epoch-1}.json"""	)				, """r"""	) as f:
												_SCREAMING_SNAKE_CASE					:			Any							= json.load(SCREAMING_SNAKE_CASE__	)
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				_SCREAMING_SNAKE_CASE					:			int							= {}
				for epoch in range(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
								model.train()
								for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= outputs.loss
												_SCREAMING_SNAKE_CASE					:			int							= loss / gradient_accumulation_steps
												accelerator.backward(SCREAMING_SNAKE_CASE__	)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_SCREAMING_SNAKE_CASE					:			int							= f"""epoch_{epoch}"""
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= os.path.join(args.output_dir				, SCREAMING_SNAKE_CASE__	)
								accelerator.save_state(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Dict							= accuracy
								_SCREAMING_SNAKE_CASE					:			Any							= lr_scheduler.get_lr()[0]
								_SCREAMING_SNAKE_CASE					:			Any							= optimizer.param_groups[0]["""lr"""]
								_SCREAMING_SNAKE_CASE					:			Dict							= epoch
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= overall_step
								accelerator.print(f"""epoch {epoch}:"""				, SCREAMING_SNAKE_CASE__	)

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir				, f"""state_{epoch}.json"""	)				, """w"""	) as f:
																json.dump(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
				parser.add_argument(
				    """--model_name_or_path"""				, type=SCREAMING_SNAKE_CASE__				, default="""bert-base-cased"""				, help="""Path to pretrained model or model identifier from huggingface.co/models."""				, required=SCREAMING_SNAKE_CASE__				, )
				parser.add_argument(
				    """--output_dir"""				, type=SCREAMING_SNAKE_CASE__				, default="""."""				, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				, )
				parser.add_argument(
				    """--resume_from_checkpoint"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If the training should continue from a checkpoint folder."""				, )
				parser.add_argument(
				    """--partial_train_epoch"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If passed, the training will stop after this number of epochs."""				, )
				parser.add_argument(
				    """--num_epochs"""				, type=SCREAMING_SNAKE_CASE__				, default=2				, help="""Number of train epochs."""				, )
				_SCREAMING_SNAKE_CASE					:			str							= parser.parse_args()
				_SCREAMING_SNAKE_CASE					:			int							= {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

				training_function(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				main()




